371 citations · 2.1k across the 193 of their papers we have counts for
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Abstractive Headline Generation for Spoken Content by Attentive Recurrent Neural Networks with ASR Error Modeling
Lang-Chi Yu, Hung-yi Lee, Lin-shan Lee
Headline generation for spoken content is important since spoken content is difficult to be shown on the screen and browsed by the user. It is a special type of abstractive summari…
Attention-based Memory Selection Recurrent Network for Language Modeling
Da-Rong Liu, Shun-Po Chuang, Hung-yi Lee
Recurrent neural networks (RNNs) have achieved great success in language modeling. However, since the RNNs have fixed size of memory, their memory cannot store all the information…
Interactive Spoken Content Retrieval by Deep Reinforcement Learning
Yen-Chen Wu, Tzu-Hsiang Lin, Yang-De Chen +2
User-machine interaction is important for spoken content retrieval. For text content retrieval, the user can easily scan through and select on a list of retrieved item. This is imp…
Towards Machine Comprehension of Spoken Content: Initial TOEFL Listening Comprehension Test by Machine
Bo-Hsiang Tseng, Sheng-Syun Shen, Hung-Yi Lee +1
Multimedia or spoken content presents more attractive information than plain text content, but it's more difficult to display on a screen and be selected by a user. As a result, ac…
Hierarchical Attention Model for Improved Machine Comprehension of Spoken Content
Wei Fang, Jui-Yang Hsu, Hung-yi Lee +1
Multimedia or spoken content presents more attractive information than plain text content, but the former is more difficult to display on a screen and be selected by a user. As a r…
Neural Attention Models for Sequence Classification: Analysis and Application to Key Term Extraction and Dialogue Act Detection
Sheng-syun Shen, Hung-yi Lee
Recurrent neural network architectures combining with attention mechanism, or neural attention model, have shown promising performance recently for the tasks including speech recog…
Audio Word2Vec: Unsupervised Learning of Audio Segment Representations using Sequence-to-sequence Autoencoder
Yu-An Chung, Chao-Chung Wu, Chia-Hao Shen +2
The vector representations of fixed dimensionality for words (in text) offered by Word2Vec have been shown to be very useful in many application scenarios, in particular due to the…
An Iterative Deep Learning Framework for Unsupervised Discovery of Speech Features and Linguistic Units with Applications on Spoken Term Detection
Cheng-Tao Chung, Cheng-Yu Tsai, Hsiang-Hung Lu +3
In this work we aim to discover high quality speech features and linguistic units directly from unlabeled speech data in a zero resource scenario. The results are evaluated using t…